Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	33,633
2	Florida	30,483
3	Mississippi	29,676
4	Arizona	28,439
5	Alabama	27,648
6	Georgia	25,673
7	South Carolina	24,587
8	Tennessee	24,294
9	Nevada	23,576
10	Texas	23,378
11	Iowa	22,994
12	New York	22,959
13	New Jersey	22,202
14	Arkansas	22,136
15	Rhode Island	21,505
16	District of Columbia	20,420
17	Illinois	20,405
18	Idaho	19,600
19	Nebraska	19,320
20	California	19,057
21	Delaware	18,963
22	North Dakota	18,956
23	Maryland	18,951
24	Massachusetts	17,924
25	South Dakota	17,898
26	Utah	17,524
27	North Carolina	17,289
28	Oklahoma	16,856
29	Kansas	16,626
30	Missouri	16,205
31	Indiana	15,511
32	Wisconsin	15,497
33	Virginia	15,291
34	Connecticut	15,172
35	Minnesota	14,593
36	Kentucky	13,083
37	New Mexico	12,604
38	Michigan	12,105
39	Ohio	11,471
40	Pennsylvania	11,461
41	Puerto Rico	11,359
42	Washington	10,784
43	Colorado	10,518
44	Alaska	9,377
45	Montana	8,134
46	Wyoming	7,255
47	Hawaii	7,250
48	Oregon	6,795
49	West Virginia	6,705
50	New Hampshire	5,569
51	Maine	3,541
52	Vermont	2,661

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	280
2	Nebraska	240
3	Iowa	212
4	Oklahoma	208
5	Missouri	203
6	South Dakota	201
7	Wisconsin	186
8	Alabama	176
9	Rhode Island	169
10	Georgia	167
11	Tennessee	164
12	Idaho	163
13	Louisiana	163
14	Kentucky	142
15	Texas	134
16	Mississippi	133
17	Illinois	129
18	Arkansas	118
19	Kansas	117
20	Utah	117
21	Virginia	115
22	Montana	110
23	Alaska	101
24	Florida	100
25	North Carolina	99
26	Wyoming	96
27	Indiana	94
28	Puerto Rico	94
29	California	87
30	Michigan	83
31	West Virginia	82
32	Hawaii	78
33	Ohio	78
34	Delaware	74
35	Washington	71
36	Connecticut	68
37	Maryland	65
38	South Carolina	63
39	Nevada	61
40	Minnesota	60
41	Pennsylvania	54
42	Arizona	47
43	District of Columbia	45
44	New Mexico	45
45	Colorado	43
46	New Jersey	38
47	Oregon	37
48	Massachusetts	36
49	New York	32
50	New Hampshire	23
51	Maine	14
52	Vermont	5

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,802
2	New York	1,676
3	Massachusetts	1,329
4	Connecticut	1,255
5	Louisiana	1,110
6	Rhode Island	1,007
7	Mississippi	892
8	District of Columbia	872
9	Arizona	724
10	Michigan	690
11	Illinois	669
12	Maryland	632
13	Delaware	629
14	Pennsylvania	616
15	South Carolina	577
16	Florida	573
17	Georgia	570
18	Indiana	506
19	Texas	482
20	Alabama	469
21	Nevada	464
22	New Mexico	389
23	Iowa	382
24	Ohio	372
25	California	356
26	Colorado	345
27	Minnesota	343
28	New Hampshire	319
29	Virginia	317
30	Arkansas	311
31	Missouri	288
32	North Carolina	287
33	Tennessee	287
34	Washington	272
35	Kentucky	240
36	Idaho	227
37	Nebraska	227
38	Oklahoma	221
39	North Dakota	213
40	Wisconsin	206
41	South Dakota	200
42	Kansas	171
43	Puerto Rico	160
44	West Virginia	143
45	Utah	134
46	Oregon	118
47	Montana	115
48	Maine	99
49	Vermont	92
50	Wyoming	72
51	Hawaii	65
52	Alaska	53

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Florida	7
2	Mississippi	7
3	Tennessee	5
4	Georgia	4
5	Iowa	4
6	Louisiana	4
7	Nebraska	4
8	Nevada	4
9	South Carolina	4
10	Arkansas	3
11	Idaho	3
12	North Carolina	3
13	Puerto Rico	3
14	Rhode Island	3
15	Arizona	2
16	California	2
17	District of Columbia	2
18	Indiana	2
19	Kentucky	2
20	Michigan	2
21	Ohio	2
22	Texas	2
23	Alabama	1
24	Delaware	1
25	Hawaii	1
26	Illinois	1
27	Kansas	1
28	Maryland	1
29	Massachusetts	1
30	Minnesota	1
31	Missouri	1
32	Montana	1
33	New Mexico	1
34	North Dakota	1
35	Oklahoma	1
36	Oregon	1
37	Pennsylvania	1
38	South Dakota	1
39	Washington	1
40	West Virginia	1
41	Wisconsin	1
42	Alaska	0
43	Colorado	0
44	Connecticut	0
45	Maine	0
46	New Hampshire	0
47	New Jersey	0
48	New York	0
49	Utah	0
50	Vermont	0
51	Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	144,718	1	99
Lafayette	Florida	144,265	2	99
Chattahoochee	Georgia	138,902	3	99
Lincoln	Arkansas	135,826	4	99
Lake	Tennessee	121,579	5	99
Davidson	Tennessee	38,880	168	94
Richland	South Carolina	30,073	336	89
York	South Carolina	16,357	1071	65
Orange	California	16,248	1084	65
Pierce	Washington	8,837	1952	37

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,730	1	99
Galax city	Virginia	4,412	2	99
Randolph	Georgia	3,983	3	99
Terrell	Georgia	3,634	4	99
Emporia city	Virginia	3,554	5	99
Richland	South Carolina	505	691	78
Davidson	Tennessee	395	925	70
Orange	California	337	1064	66
Pierce	Washington	219	1445	54
York	South Carolina	203	1514	51

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.